3 papers
cs.CL2025
Correct-Detect: Balancing Performance and Ambiguity Through the Lens of Coreference Resolution in LLMs
Amber Shore, Russell Scheinberg, Ameeta Agrawal +1
Large Language Models (LLMs) are intended to reflect human linguistic competencies. But humans have access to a broad and embodied context, which is key in detecting and resolving…
cs.CL2025
Explain-then-Process: Using Grammar Prompting to Enhance Grammatical Acceptability Judgments
Russell Scheinberg, Ameeta Agrawal, Amber Shore +1
Large language models (LLMs) can explain grammatical rules, yet they often fail to apply those rules when judging sentence acceptability. We present "grammar prompting", an explain…
cs.CL2025
Who Relies More on World Knowledge and Bias for Syntactic Ambiguity Resolution: Humans or LLMs?
So Young Lee, Russell Scheinberg, Amber Shore +1
This study explores how recent large language models (LLMs) navigate relative clause attachment {ambiguity} and use world knowledge biases for disambiguation in six typologically d…